Finding the root cause for misbehaviour of a certain product, such as a turbine, can be very time consuming, and is often characterized as a “search in blindness” in a vast amount of data available on a given turbine, or a group of turbines. The data can include a construction bill of materials (BOM), service events, parameter settings, software version, profiles, operational data, etc.
It is very difficult to search for explanations and reasons in distributed databases, and the data connected to a certain turbine is maintained in different departments of a company. The construction BOM can be located at a new unit and service alterations can be at a service department etc.
When a turbine or a group of turbines is identified to be out of normal behavior either by the turbine controller (alarm monitoring), by service engineering or by model based monitoring (dynamic limits) investigation on this is generally performed by technicians on site.
Only in special cases, remote analyses are performed. This remote analysis involves creating a picture of what has happened to this turbine since it was produced, to make sure that any event that may have caused the misbehavior is found. The data needed for this analysis is to be found in many different databases, in different departments, and in different structures. This is very time consuming, and there are substantial chances that something is missed that could point to the root cause. It actually turns out that in many cases when the root cause is known, one can also find an explanation in the kept data, but such an explanation was hidden due to the amount and complexity of the available data.
At least in view of the foregoing considerations, it is desirable to improve root cause analysis.